Joint registration and multi‐target tracking based on labelled random finite set and expectation maximisation

Li, Minzhe; Jing, Zhongliang; Pan, Han; Dong, Peng

doi:10.1049/iet-rsn.2017.0137

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…The number of observation datasets used for estimating parameter is K = 20. The number of component objects is varying in the experiment, with n k = 3 for k = 1, 2, 3, 4, 5; n k = 5 for k = 6, 7, 8, 9, 10; n k = 4 for k = 11, 12, 13, 14, 15; and n k = 3 for k = 16,17,18,19,20. The two sensors in the experiment work asynchronously: Sensor 1 provides observation datasets at odd time steps, and Sensor 2 provides observation datasets at even time steps.…”

Section: Methodsmentioning

confidence: 99%

“…In the middle 1970s, G. Matheron systematically described the random set theory [19]. The random finite set (RFS) approach introduced by Mahler is an advanced theory that unifies much of information fusion under a single Bayesian paradigm [20,21].…”

Section: Random Finite Set (Rfs)mentioning

confidence: 99%

“…By using sequential importance sampling to each terms of Equation (19), the particle approximation of Equation (19) can be obtained. After choosing the importance densities p k (·|Z k , θ) and q k (·|x k−1 , Z k , θ) for spontaneous birth and persisting objects, Equation (19) can be rewritten as…”

Section: Smc Based Implementationmentioning

confidence: 99%

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Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems

Wang

Peng

et al. 2018

Entropy

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Parameter estimation is one of the key technologies for system identification. The Bayesian parameter estimation algorithms are very important for identifying stochastic systems. In this paper, a random finite set based algorithm is proposed to overcome the disadvantages of the existing Bayesian parameter estimation algorithms. It can estimate the unknown parameters of the stochastic system which consists of a varying number of constituent elements by using the measurements disturbed by false detections, missed detections and noises. The models used for parameter estimation are constructed by using random finite set. Based on the proposed system model and measurement model, the key principles and formula derivation of the proposed algorithm are detailed. Then, the implementation of the algorithm is presented by using sequential Monte Carlo based Probability Hypothesis Density (PHD) filter and simulated tempering based importance sampling. Finally, the experiments of systematic errors estimation of multiple sensors are provided to prove the main advantages of the proposed algorithm. The sensitivity analysis is carried out to further study the mechanism of the algorithm. The experimental results verify the superiority of the proposed algorithm.

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Section: Methodsmentioning

confidence: 99%

Section: Random Finite Set (Rfs)mentioning

confidence: 99%

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Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems

Wang

Peng

et al. 2018

Entropy

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“…A new analytical algorithm is developed in [72] to address the problem of joint sensor registration and multi-target tracking based on the labeled RFS and expectation maximization. A new complete data log-likelihood function is defined with the measurement and state RFS variables.…”

Section: Joint Registration and Multi-target Tracking Based On Labelementioning

confidence: 99%

“…The performance of the proposed algorithms based on the labeled RFS is shown, and compared with that of traditional methods based on the unlabeled RFS. The simulation scenario is similar as [72,75,76]. Suppose that there are several targets with two possible classes move in a two-dimensional scenario.…”

Section: Performance Comparisonmentioning

confidence: 99%

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Jing

Leung

2018

Self Cite

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Multi-target detection, tracking and classification are important problems in aerospace applications, such as reconnaissance, airborne and spaceborne sensing. These problems are correlated but are difficult to be solved simultaneously, especially for systems with multiple sensors. This paper summarized the existing work for multi-target joint detection, tracking and classification based on labeled random finite set. Furthermore, a new algorithm is proposed for multi-sensor multi-target joint detection, tracking and classification problem. A conditional multi-sensor multi-target state estimator is derived, and the optimal solution is then obtained based on the minimum Bayes risk criterion. The numerical simulations are performed, and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set. It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection, tracking and classification.

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Multi-target Tracking by Asynchronous Sensors Based on Extended Labeled Multi-Bernoulli Filter with Doppler Measurement

Lai,

Xiong,

2024

Lecture Notes in Electrical Engineering

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Joint registration and multi‐target tracking based on labelled random finite set and expectation maximisation

Cited by 9 publications

References 26 publications

Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems

Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Multi-target Tracking by Asynchronous Sensors Based on Extended Labeled Multi-Bernoulli Filter with Doppler Measurement

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